alt Hacker News>Starting with 4 virtual cores and 8 GB vRAM, where the VM ran perfectly briskly with around 5 GB of memory used, I stepped down to 3 cores and 6 GB, to discover that memory usage fell to 3.9 GB and everything worked well. With just 2 cores and 4 GB of memory only 3.1 GB of that was used, and the VM continued to handle those lightweight tasks normally.
Good reminder that there's a certain amount of memory tied up with each core (probably mainly page cache and concurrency handling etc).
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I'd bet for the null hypothesis: the memory behaviour changes would hold if the core count was kept constant and only the VM's memory size was adjusted.
There is some overhead per-core, you're right, but imo this reduction in usage is likely from how the kernel allocates available memory, which is being reduced as well. The kernel will keep read caches around longer with more memory, it'll prefer to compress memory instead of swap to disk if it has more, it'll purge/cleanup reclaimable memory less often with more memory, etc. It even scales its internal buffer sizes and vnode tables depending on total memory.
All good things imo, it dynamically makes the most of what is available, at the expense of making it harder to see a true baseline of hard min requirement to operate.
Fun things to check, `vm_stat`
$ vm_stat Mach Virtual Memory Statistics: (page size of 4096 bytes)
Pages free: 230295.
Pages active: 1206857.
Pages inactive: 1206361.
Pages speculative: 31863.
Pages throttled: 0.
Pages wired down: 470093.
Pages purgeable: 18894.
"Translation faults": 21635255.
Pages copy-on-write: 1590349.
Pages zero filled: 11093310.
Pages reactivated: 15580.
Pages purged: 50928.
File-backed pages: 689378.
Anonymous pages: 1755703.
Pages stored in compressor: 0.
Pages occupied by compressor: 0.
Decompressions: 0.
Compressions: 0.
Pageins: 832529.
Pageouts: 225.
Swapins: 0.
Swapouts: 0.
edit: no code fence markdown support or am I doing something wrong?
As a general rule, also the amount of physical memory installed in a computer should be proportional with the number of hardware threads provided by its CPU.
Besides the fact that the operating system may allocate some memory for each thread, when you launch a multi-threaded application that is able to use all available threads, for instance the compilation of a big software project, it frequently will allocate some working memory in an amount proportional with the amount of working threads.
I have encountered many multi-threaded applications that need up to 2 GB per thread to work well.
This corresponds to having 64 GB for a desktop CPU with 32 threads, like Ryzen 9 9950X.
For the compilation example, I have seen software projects, like Chrome/Chromium and its derivatives, where if you do not have enough memory, proportional to the number of hardware threads, e.g. when you have only 32 GB for a 16 core/32 thread CPU, you must reduce the number of concurrent compilations, e.g. with an appropriate parameter to "make -j", leaving some threads and cores idle, because otherwise you may encounter out-of-memory errors.